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Artificial Intelligence research seeking “Strong AI” – essentially a self aware, conscious computing system where the machine would think as effectively as humans, had languished for many years after initial hype failed to pan out.

Thankfully, this line of research is now coming back online in a very big way, with enough funding and supercomputing power to usher in the biggest technology breakthrough in the history of humanity. Thinking computers would revolutionize the world in many ways, allowing much more efficient resource development, utilization, and conservation. Barring the unlikely scenario of an “unfriendly AI” that would seek to destroy humanity, a “friendly AI” would bring massively faster problem solving and, very likely, would improve at a spectacular rate leading to greater and greater technological breakthroughs.

Europe, however, has now pledged a billion Euro to the Human Brain Project under the stewardship of the man who is arguably the most knowledgeable in this field, Henry Markram. You can read Dr. Markram’s guest post here at Technology Report comparing his original “Blue Brain” project with the Darpa SyNAPSE efforts in the USA.

Although the “cat brain controversy” was somewhat contentious, we’re hoping Dr. Markram’s desire to cooperate with others across the brain project spectrum – much as happened during the human genome project – will become the standard.

The Brain Activity Map Project is a major effort to map *every neuron” in the human brain, and may represent a significant advance towards conscious machine intelligence sometimes called “Strong General Artificial Intelligence” or “Strong AI”. The Brain Activity Map is expected to get Obama administration funding to the tune of approximately 3 billion over the next decade.

The project has some similarities to Europe’s new “Human Brain Project” effort, a well funded extension of Henry Markram’s “Blue Brain Project” which seeks to create a working model of the human brain in the next decade.

At these funding levels technological optimists can hope for even more dramatic progress towards the technological singularity, the point at which machine intelligence far surpasses that of humans. Futurist and engineer Ray Kurzweil has written extensively about the coming technological singularity, and his recent appointment as Google’s director of engineering suggests that the private sector will also be funding machine consciousness projects at unprecedented levels.

Countdown to the SINGULARITY?

Technology report has been focused on this topic for some time, and it’s now fair to say that funding levels are starting to match the enormous potential of machine intelligence to revolutionize virtually every aspect of human existence. Although some pessimists fear the worst, we think it’s very likely that superintelligences will either ignore us or help humankind solve the many problems we face. The likelihood of a hostile superintelligence or “unfriendly AI” seems low.

In January Henry Markram got a late Christmas present. After intense international competition, Markram’s quest for a brain simulation received one of the largest grants in the history of science – 500 million euros from Europe’s new Technology “Flagship” program.

The European Human Brain project is a large expansion of Markram’s “Blue Brain” efforts which have made amazing progress over the past several years. With this level of funding the HBP appears to have left the USA’s DARPA SyNAPSE as something of a funding pauper. However as politicians begin to recognize the significance of thinking computers DARPA is likely to get much higher funding.

From the HBP Executive Summary:

We propose that the HBP should be organised in three phases, lasting a total of ten years. For the first two and a half years (the “ramp-up” phase), the project should focus on setting up the initial versions of the ICT platforms and on seeding them with strategically selected data. At the end of this phase, the platforms should be ready for use by researchers inside and outside the project.For the following four and a half years (the “operational phase”), the project should intensify work to generate strategic data and to add new capabilities to the platforms, while simultaneously demonstrating the value of the platforms for basic neuroscience research and for applications in medicine and future computing technology.In the last three years (the “sustainability phase”), the project should continue these activities while simultaneously moving towards financial self-sustainability – ensuring that the capabilities and knowledge it has created become a permanent asset for European science and industry

Inventor and futurist Ray Kurzweil is generally regarded as one of the world’s top engineers working on Artificial Intelligence, and he’s certainly the world’s top *evangelist* for AI, arguing that general AI, or thinking machines, will inevitably arise, and fairly soon, as another step down the evolutionary path of the human species. His book “The Singularity is Near”, is the key popular work addressing what many believe will become the biggest technological theme in history – the creation of an intelligent computer that is capable of human-like thought processes.

Bill Gates has called Ray Kurzweil the leading thinker in the area of artificial intelligence.

Google very recently hired Kurzweil as Director of Engineering, promising a marriage of his ideas with the company that is probably best suited to fund and deploy general AI applications.

Here, in an interview at Singularity Hub, Kurzweil discusses Google’s role in the advancement of AI:

Here at Technology Report we recognize that a conscious computer is likely to 1. happen within decades and 2. bring the most profound transformation of humanity in the history of … humanity. Two of the major projects working towards the goal of a general artificial intelligence are DARPA SYNAPSE and BLUE BRAIN. DARPA’s funding is much higher but they are newer and approaching the problem more along the lines of *computer programming* rather than *reverse engineering*. Blue Brain’s approach is more along the lines of copying the observed neural structure of the human brain and creating a computer simulation based on those observations and activity. Here, from their website, is a progress report on Blue Brain:

The Blue Brain Project plans to reverse engineer the human brain as a supercomputer simulation.

The project was founded in May 2005 by Henry Markram at the EPFL in Switzerland and has made notable progress in its first decade.

As of August 2012 the largest simulations are of mesocircuits containing around 100 cortical columns (image above right). Such simulations involve approximately 1 million neurons and 1 billion synapses. This is about the same scale as that of a honey bee brain. It is hoped that a rat brain neocortical simulation (~21 million neurons) will be achieved by the end of 2014. A full human brain simulation (86 billion neurons) should be possible by 2023 provided sufficient funding is received.

Latest news

July 9, 2012 –

The FET Flagship Pilots final conference took place in Brussels today. Results of the recently-completed one-year pilot phase of the Human Brain Project (HBP) were presented. See the 108-page HBP report, as well as the conference statement by EC vice-president Neelie Kroes. During autumn 2012 the EU will consider the HBP and five other candidate science projects. In February 2013 a decision will be made on which of the two candidates will each receive €1 billion in funding over ten years. The chosen two projects will then run from 2013 to 2023. If the HBP is chosen, the Blue Brain Project will become a central part of it.

Jun 20, 2012 –

Two newly published video talks which share lots of detail about the Blue Brain Project simulations and visualisations. The talks were given at the INCF Multiscale Modeling Program Workshop in Stockholm on May 31 and June 1, 2012.

We’ll be keeping tabs on the human connectome project as well as the remarkable DARPA SyNAPSE and BLUE BRAIN. Connectome is working to map the human brain, and they already have a very interesting gallery and information online:

An early goal of the Human Connectome project is to discover how a relatively small number of genes can define such complex structure. One explanation offered by researchers there is to compare the brain to a big city where a huge “two dimensional” flat network of streets interacts with three dimensional buildings. As in the brain a relatively simple and somewhat repetitive structure can branch into enormous complexity and possibilities.

For me it’s one of the world’s most significant and intriguing science projects along with DARPA SyNAPSE which is also working towards strong AI but in a very different way.

Using an IBM Blue Gene Supercomputer, Blue Brain is working to create a simulation of many of the interactions in the Neocortex of the brain (think “where we think”). Progress after a few years of the project seems pretty steady although DARPA SyNAPSE has been a lot more money lately as it dips into the massive resources of the US military.

IBM’s Aug 18th Press Release announced another significant milestone for the DARPA SyNAPSE project, the world’s best funded and arguably the “most likely to succeed” approach to creating a general artificial intelligence.

The release notes that the new chips represent a departure from traditional models of computing:

…. cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

To do this, IBM is combining principles from nanoscience, neuroscience and supercomputing as part of a multi-year cognitive computing initiative. The company and its university collaborators also announced they have been awarded approximately $21 million in new funding from the Defense Advanced Research Projects Agency (DARPA) for Phase 2 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.

As we’ve noted here many times, another remarkable project is the Blue Brain Project in Europe spearheaded by Dr. Henry Markram. That team has joined with many others and is in the process of applying to the European Union for substantial funding – perhaps as much as 1.6 billion dollars. Although Blue Brain tends to shy away from stating that their objective is a general artificial intelligence, I would argue that they should have that goal and also that they are much more likely to be funded by stating that goal in no uncertain terms.

Unfortunately there remain many both in and outside of technology circles who believe the search for a general artificial intelligence is either dangerous or a waste of time and money. Both these scenarios are possible but unlikely. Sure, intelligence can be dangerous but given human history compared to technology history it seems odd to argue that we are more likely to create a Frankenstein than a helpful machine process. Computers don’t kill people, people kill people.

In terms of a waste of time and money, clearly we humans have overrated our intelligence for some time – probably since the beginning of self-awareness. There are few rational reasons to reject the idea that we cannot duplicate processes that are similar to our own thinking in a machine. The advantages of machine based intelligence are likely to be substantial – probably on the order of a new human age with vastly improved resource efficiency, poverty reduction, and more. Thus the costs – currently measured in the low tens of millions – pale in comparison to almost all other government projects – many with massively dubious and negative ROIs.

Dr. Dharmendra Modha and his SyNAPSE gang recently published an excellent paper about “Cognitive Computing” that updates what appears to be excellent progress in the effort to create a general artificial intelligence:

One of the paper’s most notable items asserts that within a decade the project expects to have the computational scale needed for human level modelling, though it also notes that this is not the same as creating a model of the human brain – this may require computational structures yet to be invented. However on balance it would seem the SyNAPSE project continues to build on their core assumptions, taking us ever closer to the holy grail of technology – a general artificial intelligence.

More at Dr. Modha’s blog , where we learn more about the new approaches the SyNAPSE team at IBM will take in an effort to achieve human quality cognition in a machine:

18 Aug 2011: Today, IBM (NYSE: IBM) researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. The technology could yield many orders of magnitude less power consumption and space than used in today’s computers.

In a sharp departure from traditional concepts in designing and building computers, IBM’s first neurosynaptic computing chips recreate the phenomena between spiking neurons and synapses in biological systems, such as the brain, through advanced algorithms and silicon circuitry. Its first two prototype chips have already been fabricated and are currently undergoing testing.

Called cognitive computers, systems built with these chips won’t be programmed the same way traditional computers are today. Rather, cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

Here, from PBS, is an interesting interview with Marvin Minsky, one of the key pioneers of Artificial Intelligence research. Although Minsky remains somewhat optimistic about developing a general artificial intelligence, he believes that the current approaches are misguided and too narrow – that researchers are now looking for “a magic bullet”, and that it’s going to take a lot longer to create generalized AI than if we applied a more general approach:

How hard is it to build an intelligent machine? I don’t think it’s so hard …. The basic idea I promote is that you mustn’t look for a magic bullet. You mustn’t look for one wonderful way to solve all problems. Instead you want to look for 20 or 30 ways to solve different kinds of problems. And to build some kind of higher administrative device that figures out what kind of problem you have and what method to use.

Now, if you take any particular researcher today, it’s very unlikely that that researcher is going to work on this architectural level of what the thinking machine should be like. Instead a typical researcher says, “I have a new way to use statistics to solve all problems.” Or: “I have a new way to make a system that imitates evolution. It does trials and finds the things that work and remembers the things that don’t and gets better that way.” And another one says, “It’s going to use formal logic and reasoning of a certain kind, and it will figure out everything.” So each researcher today is likely to have one particular idea, and that researcher is trying to show that he or she can make a machine that will solve all problems in that way.

I think this is a disease that has spread through my profession. Each practitioner thinks there’s one magic way to get a machine to be smart, and so they’re all wasting their time in a sense.

I was surprised to see his lack of optimism in the face of so much progress in areas I’d argue are very generalized indeed. The DARPA SyNAPSE project we’ve discussed several times here at Technology Report remains the best funded AI research to date, and lead researchers seem to feel optimistic that progress there could lead to a human scale general intelligence within several years rather than several decades that Minsky implies may be required given the new approaches.

Simply put, DARPA SyNAPSE is creating a computing infrastructure to rival the human brain in terms of connectivity, and counting on the possibility that we are dealing mostly with *quantity of connections* rather than *quality of connections* when we talk about human level intelligence.

The other very promising project for generalized AI is somewhat at odds with the DARPA SyNAPSE view. The Blue Brain project is also a promising development ground for general artificial intelligence, but the approach is very different as described by Dr. Henry Markram, the project manager at Blue Brain. The Blue Brain team is focusing more on “reverse engineering” animal brains and eventually a human brain.

Given the new level of enthusiasm and funding from DARPA, it seems likely that progress will continue at a faster pace that at anytime in the past.

Ironically I think Minsky’s early optimism in the 1950s was more justified than his current pessimism, though his observation that academics are working in too much isolation is certainly true. I’m often surprised how many technologists don’t seem to understand many simple aspects of human biology and evolution and vica versa. Human intelligence, though intriguing, continues to be overrated as an phenomenon of exceptional quality. We’re a somewhat arrogant creature by evolutionary design, but that does not justify our self importance. Machines surpass most of us in most compartmentalized aspects of intelligence and many aspects of creativity (mathematics / translation and language / game playing / music / information retrival, etc, etc). It seems reasonable that what we call “consciousness” may only require massive connectivity – perhaps something as simple as creating a fast, multitasked conversation between different parts of an artificial brain.